Towards a New Form of Undemocratic Capitalism: Introducing Macro-Equity to Finance Development Post COVID-19 Crisis
Bibliographic record
Abstract
Sustainable Development Goal 16 talks about Peace, Justice, and Strong Institutions, and goal 10 talks about reducing inequality. A major problem exposed by the COVID-19 crisis is that public deficits seem to be the normal state in the business cycle’s booms and downturns, limiting capacity for emergencies. Corporate capitalism has an incentive to perpetuate deficits to increase growth, provide risk-free interest income to financial institutions, and to increase inequalities and economic injustice. To counter this problem, the purpose of this communication is to suggest that countries need to issue equity capital, which we term macro-equity. This macro-equity will give dividends to its shareholders in times of public surplus and issue new shares in times of public deficits. The communication is written as a mind experiment, debating the issues that may arise. This proposal raises many questions of an ethical and moral nature that will lead to passionate debate. The use of macro-equity will reduce countries’ stress, created by high public debt. With appropriate incentives, it may create an entrepreneurial mindset in political leaders that may even reduce corruption and promote redistribution. The moral and ethical issues need to be weighed against the street violence in the absence of any change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".